{
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  "metadata": {
    "colab": {
      "name": "hw13_meta_regression.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Iallen520/lhy_DL_Hw/blob/master/hw13_meta_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yow9nZCqtvjj",
        "colab_type": "text"
      },
      "source": [
        "# **Homework 13 - Meta Learning**\n",
        "\n",
        "若有任何問題，歡迎來信至助教信箱 ntu-ml-2020spring-ta@googlegroups.com"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWbkcznitlaF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.utils.data as data\n",
        "import torch.nn.functional as F\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "import copy\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSGlyeWsxTDI",
        "colab_type": "text"
      },
      "source": [
        "生成 $ a*\\sin(x+b) $ 的資料點，其中$a, b$的範圍分別預設為$[0.1, 5], [0, 2\\pi]$，每一個 $ a*\\sin(x+b) $的函數有10個資料點當作訓練資料。測試時則可用較密集的資料點直接由畫圖來看generalize的好壞。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4tjWphivAUD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "device = 'cpu'\n",
        "def meta_task_data(seed = 0, a_range=[0.1, 5], b_range = [0, 2*np.pi], task_num = 100,\n",
        "                   n_sample = 10, sample_range = [-5, 5], plot = False):\n",
        "    np.random.seed = seed\n",
        "    a_s = np.random.uniform(low = a_range[0], high = a_range[1], size = task_num)\n",
        "    b_s = np.random.uniform(low = b_range[0], high = b_range[1], size = task_num)\n",
        "    total_x = []\n",
        "    total_y = []\n",
        "    label = []\n",
        "    for t in range(task_num):\n",
        "        x = np.random.uniform(low = sample_range[0], high = sample_range[1], size = n_sample)\n",
        "        total_x.append(x)\n",
        "        total_y.append( a_s[t]*np.sin(x+b_s[t]) )\n",
        "        label.append('{:.3}*sin(x+{:.3})'.format(a_s[t], b_s[t]))\n",
        "    if plot:\n",
        "        plot_x = [np.linspace(-5, 5, 1000)]\n",
        "        plot_y = []\n",
        "        for t in range(task_num):\n",
        "            plot_y.append( a_s[t]*np.sin(plot_x+b_s[t]) ) \n",
        "        return total_x, total_y, plot_x, plot_y, label\n",
        "    else:\n",
        "        return total_x, total_y, label"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjKn7O5H0WMA",
        "colab_type": "text"
      },
      "source": [
        "以下我們將老師MAML投影片第27頁的$\\phi$稱作meta weight，$\\theta$則稱為sub weight。\n",
        "\n",
        "老師投影片： http://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2019/Lecture/Meta1%20(v6).pdf\n",
        "\n",
        "***\n",
        "\n",
        "為了讓sub weight的gradient能夠傳到meta weight  (因為sub weight的初始化是從meta weight來的，所以想當然我們用sub weight算出來的loss對meta weight也應該是可以算gradient才對)，這邊我們需要重新定義一些pytorch內的layer的運算。\n",
        "\n",
        "實際上*MetaLinear*這個class做的事情跟torch.nn.Linear完全是一樣的，唯一的差別在於這邊的每一個tensor都沒有被變成torch.nn.Parameter。這麼做的原因是因為等一下我們從meta weight那裏複製(init weight輸入meta weight後weight與bias使用.clone)的時候，tensor的clone的操作是可以傳遞gradient的，以方便我們用gradient更新meta weight。這個寫法的代價是我們就沒辦法使用torch.optim更新sub weight了，因為參數都只用tensor紀錄。也因此我們接下來需要自己寫gradient update的函數(只用SGD的話是簡單的)。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCagZZYPwr6p",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MetaLinear(nn.Module):\n",
        "    def __init__(self, init_layer = None):\n",
        "        super(MetaLinear, self).__init__()\n",
        "        if type(init_layer) != type(None):\n",
        "            self.weight = init_layer.weight.clone()\n",
        "            self.bias = init_layer.bias.clone()\n",
        "\n",
        "    def zero_grad(self):\n",
        "        self.weight.grad  = torch.zeros_like(self.weight)\n",
        "        self.bias.grad  = torch.zeros_like(self.bias)\n",
        "        \n",
        "    def forward(self, x):\n",
        "        return F.linear(x, self.weight, self.bias)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LJBv53j53xIp",
        "colab_type": "text"
      },
      "source": [
        "這裡的forward和一般的model是一樣的，唯一的差別在於我們需要多寫一下\\_\\_init\\_\\_函數讓他比起一般的pytorch model多一個可以從meta weight複製的功能(這邊因為我把model的架構寫死了所以可能看起來會有點多餘，讀者可以自己把net()改成可以自己調整架構的樣子，然後思考一下如何生成一個跟meta weight一樣形狀的sub weight)\n",
        "\n",
        "update函數就如同前一段提到的，我們需要自己先手動用SGD更新一次sub weight，接著再使用下一步的gradient(第二步)來更新meta weight。zero_grad函數在此處沒有用到，因為實際上我們計算第二步的gradient時會需要第一步的grad，這也是為什麼我們第一次backward的時候需要create_graph=True  (建立計算圖以計算二階的gradient)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FdInqRZ93oiG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class net(nn.Module):\n",
        "    def __init__(self, init_weight=None):\n",
        "        super(net, self).__init__()\n",
        "        if type(init_weight) != type(None):\n",
        "            for name, module in init_weight.named_modules():\n",
        "                if name != '':\n",
        "                    setattr(self, name, MetaLinear(module))\n",
        "        else:\n",
        "            self.hidden1 = nn.Linear(1, 40)\n",
        "            self.hidden2 = nn.Linear(40, 40)\n",
        "            self.out = nn.Linear(40, 1)\n",
        "    \n",
        "    def zero_grad(self):\n",
        "        layers = self.__dict__['_modules']\n",
        "        for layer in layers.keys():\n",
        "            layers[layer].zero_grad()\n",
        "\n",
        "    def update(self, parent, lr = 1):\n",
        "        layers = self.__dict__['_modules']\n",
        "        parent_layers = parent.__dict__['_modules']\n",
        "        for param in layers.keys():\n",
        "            layers[param].weight = layers[param].weight - lr*parent_layers[param].weight.grad\n",
        "            layers[param].bias = layers[param].bias - lr*parent_layers[param].bias.grad\n",
        "        # gradient will flow back due to clone backward\n",
        "        \n",
        "    def forward(self, x):\n",
        "        x = F.relu(self.hidden1(x))\n",
        "        x = F.relu(self.hidden2(x))\n",
        "        return self.out(x)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LB06F2Fy7gHc",
        "colab_type": "text"
      },
      "source": [
        "前面的class中我們已經都將複製meta weight到sub weight，以及sub weight的更新，gradient的傳遞都搞定了，meta weight自己本身的參數就可以按照一般pytorch model的模式，使用torch.optim來更新了。\n",
        "\n",
        "gen_model函數做的事情其實就是產生N個sub weight，並且使用前面我們寫好的複製meta weight的功能。\n",
        "\n",
        "注意到複製weight其實是整個code的關鍵，因為我們需要將sub weight計算的第二步gradient正確的傳回meta weight。讀者從meta weight與sub weight更新參數作法的差別(手動更新/用torch.nn.Parameter與torch.optim)可以再思考一下兩者的差別。\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwwEtUXU7fm0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class Meta_learning_model():\n",
        "    def __init__(self, init_weight = None):\n",
        "        super(Meta_learning_model, self).__init__()\n",
        "        self.model = net().to(device)\n",
        "        if type(init_weight) != type(None):\n",
        "            self.model.load_state_dict(init_weight)\n",
        "        self.grad_buffer = 0\n",
        "\n",
        "    def gen_models(self, num, check = True):\n",
        "        models = [net(init_weight=self.model).to(device) for i in range(num)]\n",
        "        return models\n",
        "        \n",
        "    def clear_buffer(self):\n",
        "        print(\"Before grad\", self.grad_buffer)\n",
        "        self.grad_buffer = 0"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jpxeb_ew8qb-",
        "colab_type": "text"
      },
      "source": [
        "接下來就是生成訓練/測試資料，建立meta weightmeta weight的模型以及用來比較的model pretraining的模型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b376YYA1vF9a",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bsz = 10\n",
        "\n",
        "train_x, train_y, train_label = meta_task_data(task_num=5000*10) \n",
        "train_x = torch.Tensor(train_x).unsqueeze(-1) # add one dim\n",
        "train_y = torch.Tensor(train_y).unsqueeze(-1)\n",
        "train_dataset = data.TensorDataset(train_x, train_y)\n",
        "train_loader = data.DataLoader(dataset=train_dataset, batch_size=bsz, shuffle=False)\n",
        "\n",
        "test_x, test_y, plot_x, plot_y, test_label = meta_task_data(task_num=1, n_sample = 10, plot=True)  \n",
        "test_x = torch.Tensor(test_x).unsqueeze(-1) # add one dim\n",
        "test_y = torch.Tensor(test_y).unsqueeze(-1) # add one dim\n",
        "plot_x = torch.Tensor(plot_x).unsqueeze(-1) # add one dim\n",
        "test_dataset = data.TensorDataset(test_x, test_y)\n",
        "test_loader = data.DataLoader(dataset=test_dataset, batch_size=bsz, shuffle=False)  \n",
        "\n",
        "meta_model = Meta_learning_model()\n",
        "meta_optimizer = torch.optim.Adam(meta_model.model.parameters(), lr = 1e-3)\n",
        "\n",
        "pretrain = net()\n",
        "pretrain.to(device)\n",
        "pretrain.train()\n",
        "pretrain_optim = torch.optim.Adam(pretrain.parameters(), lr = 1e-3)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOVC8J2z9UUO",
        "colab_type": "text"
      },
      "source": [
        "進行訓練，注意一開始我們要先生成一群sub weight(code裡面的sub models)，然後將一個batch的不同的sin函數的10筆資料點拿來訓練sub weight。注意這邊sub weight計算第一步gradient與第二步gradient時使用各五筆不重複的資料點(因此使用[:5]與[5:]來取)。但在訓練model pretraining的對照組時則沒有這個問題(所以pretraining的model是可以確實的走兩步gradient的)\n",
        "\n",
        "每一個sub weight計算完loss後相加(內層的for迴圈)後就可以使用optimizer來更新meta weight，再次提醒一下sub weight計算第一次loss的時候backward是需要create_graph=True的，這樣計算第二步gradient的時候才會真的計算到二階的項。讀者可以在這個地方思考一下如何將這段程式碼改成MAML的一階做法。"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyC2Mv86vP8l",
        "colab_type": "code",
        "outputId": "c5b2310f-fc69-435e-a0c2-26eb51fd1ddc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "tags": []
      },
      "source": [
        "epoch = 1\n",
        "\n",
        "for e in range(epoch):\n",
        "    meta_model.model.train()\n",
        "    for x, y in tqdm(train_loader):\n",
        "        x = x.to(device) # torch.Size([10, 10, 1])\n",
        "        y = y.to(device) # torch.Size([10, 10, 1])\n",
        "        sub_models = meta_model.gen_models(bsz)\n",
        "\n",
        "        meta_l = 0\n",
        "        for model_num in range(len(sub_models)):\n",
        "            sample = torch.randint(0, 10, size = (10,), dtype = torch.long)\n",
        "            \n",
        "            #pretraining\n",
        "            pretrain_optim.zero_grad()\n",
        "            y_tilde = pretrain(x[model_num][sample[:5],:])\n",
        "            little_l = F.mse_loss(y_tilde, y[model_num][sample[:5],:])\n",
        "            little_l.backward()\n",
        "            pretrain_optim.step()\n",
        "            pretrain_optim.zero_grad()\n",
        "            y_tilde = pretrain(x[model_num][sample[5:],:])\n",
        "            little_l = F.mse_loss(y_tilde, y[model_num][sample[5:],:])\n",
        "            little_l.backward()\n",
        "            pretrain_optim.step()\n",
        "            \n",
        "            # meta learning\n",
        "            y_tilde = sub_models[model_num](x[model_num][sample[:5],:])\n",
        "            little_l = F.mse_loss(y_tilde, y[model_num][sample[:5],:])\n",
        "            #計算第一次gradient並保留計算圖以接著計算更高階的gradient\n",
        "            little_l.backward(create_graph = True)\n",
        "            sub_models[model_num].update(lr = 1e-2, parent = meta_model.model)\n",
        "            #先清空optimizer中計算的gradient值(避免累加)\n",
        "            meta_optimizer.zero_grad()\n",
        "            \n",
        "            #計算第二次(二階)的gradient，二階的原因來自第一次update時有計算過一次gradient了\n",
        "            y_tilde = sub_models[model_num](x[model_num][sample[5:],:])\n",
        "            meta_l =  meta_l + F.mse_loss(y_tilde, y[model_num][sample[5:],:])\n",
        "\n",
        "        meta_l = meta_l / bsz\n",
        "        meta_l.backward()\n",
        "        meta_optimizer.step()\n",
        "        meta_optimizer.zero_grad()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SQ4g_qA_gtr",
        "colab_type": "text"
      },
      "source": [
        "測試我們訓練好的meta weight"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iHr-BPWEv9W_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_model = copy.deepcopy(meta_model.model)\n",
        "test_model.train()\n",
        "test_optim = torch.optim.SGD(test_model.parameters(), lr = 1e-3)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KccSKypP_nMT",
        "colab_type": "text"
      },
      "source": [
        "先畫出待測試的sin函數，以及用圓點點出測試時給meta weight訓練的十筆資料點"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "abeYF21Ev_tU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig = plt.figure(figsize = [9.6,7.2])\n",
        "ax = plt.subplot(111)\n",
        "plot_x1 = plot_x.squeeze().numpy()\n",
        "ax.scatter(test_x.numpy().squeeze(), test_y.numpy().squeeze())\n",
        "ax.plot(plot_x1, plot_y[0].squeeze())"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x7fa2f20dc5c0>]"
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P6THCM8G_0pM",
        "colab_type": "text"
      },
      "source": [
        "分別利用十筆資料點更新meta weight以及pretrained model一個step"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ji6B2UuewDgt",
        "colab_type": "code",
        "outputId": "ee1cbc41-d9b8-4b44-85f8-351bcf4cbc95",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "tags": []
      },
      "source": [
        "test_model.train()\n",
        "pretrain.train()\n",
        "\n",
        "for epoch in range(1):\n",
        "    for x, y in test_loader:\n",
        "        y_tilde = test_model(x[0])\n",
        "        little_l = F.mse_loss(y_tilde, y[0])\n",
        "        test_optim.zero_grad()\n",
        "        little_l.backward()\n",
        "        test_optim.step()\n",
        "        print(\"(meta)))Loss: \", little_l.item())\n",
        "\n",
        "for epoch in range(1):\n",
        "    for x, y in test_loader:\n",
        "        y_tilde = pretrain(x[0])\n",
        "        little_l = F.mse_loss(y_tilde, y[0])\n",
        "        pretrain_optim.zero_grad()\n",
        "        little_l.backward()\n",
        "        pretrain_optim.step()\n",
        "        print(\"(pretrain)Loss: \", little_l.item())"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "(meta)))Loss:  1.616558313369751\n(pretrain)Loss:  3.377084255218506\n"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NVp1LUdVABZH",
        "colab_type": "text"
      },
      "source": [
        "將更新後的模型所代表的函數繪製出來，與真實的sin函數比較"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-74T_CrXwMJ_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_model.eval()\n",
        "pretrain.eval()\n",
        "\n",
        "plot_y_tilde = test_model(plot_x[0]).squeeze().detach().numpy()\n",
        "plot_x2 = plot_x.squeeze().numpy()\n",
        "ax.plot(plot_x2, plot_y_tilde, label = 'tune(disjoint)')\n",
        "ax.legend()\n",
        "fig.show()"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tnc3Lr82wRtS",
        "colab_type": "code",
        "outputId": "57ad6c5a-59e4-4fae-ef32-833217e7d965",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "plot_y_tilde = pretrain(plot_x[0]).squeeze().detach().numpy()\n",
        "plot_x2 = plot_x.squeeze().numpy()\n",
        "ax.plot(plot_x2, plot_y_tilde, label = 'pretrain')\n",
        "ax.legend()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": "<matplotlib.legend.Legend at 0x7fa2f1b95828>"
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8SLgC1SAJnC",
        "colab_type": "text"
      },
      "source": [
        "執行底下的cell以顯示圖形，並重複執行更新meta weight與pretrained model的cell來比較多更新幾步後是否真的能看出meta learning比model pretraining有效"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jF5KQMHy6bt7",
        "colab_type": "code",
        "outputId": "87deaf94-8d3a-477c-ad44-1a06f30eeba4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        }
      },
      "source": [
        "fig"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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\n"
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}